Tracking Control of Underactuated Unmanned Surface Vessels Based on the Dynamic Fuzzy Neural Network

Abstract:

This paper proposes a dynamic fuzzy neural network (DFNN) for the tracking control of the underactuated unmanned surface vessels. The dynamic fuzzy neural network control algorithm has the advantages of both fuzzy logical and neural network. The algorithm adjusts the structure and parameters on line at the same time to make the tracking effect of the system being fast and accurate, while it doesn’t need confirm the fuzzy rules and the nodes of hidden layer. The simulation experiments based on the proposed control algorithm are carried out and the simulation results validate its effectiveness.

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 2188-2196
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.2188
• Online since: August 2012
• Authors: Yun Yan Hu, Guo Cheng Zhang, Lei Wan
• Keywords: Dynamic Fuzzy Neural Network, Tracking Control, Underactuated Surface Vessels

References

[1] Dirk Weidemann, Norbert Scherm, Bodo Heimann, and Houssem Abdellatif, Nonlinear discrete-time control approaches for underactuated manipulators, Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, Seville, Spain, p.4379–4384, Dec. (2005).

DOI: 10.1109/cdc.2005.1582851

[2] Wenjie Dong and Yi Guo, Glogal Time-varying Stabilization of Underactuated Surface Vessel,, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, vol. 50, no. 6. JUNE, 2005, p.859–864.

DOI: 10.1109/tac.2005.849248

[3] Islam I. Hussein and ANTHONY M. Bloch, Optimal Control of Underactuated Nonholonomic Mechanical Systems, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, vol. 53, no. 3. April, 2008, 1963, p.668–682.

DOI: 10.1109/tac.2008.919853

[4] K.D. Do, J. Pan and Z.P. Jiang, Global exponential tracking control of underactuated surface ships in the body frame, Proceedings of the American Control Conference, Anchorage, AK, May, 2002, pp: 4702-4707.

DOI: 10.1109/acc.2002.1025400

[5] Changliang Xia, Chen Guo, and Tingna Shi, A Neural-Network-Identifier and Fuzzy-Controller-Based Algorithm for Dynamic Decoupling Control of Permanent-Magnet Spherical Motor, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 57, no. 8, August 2010, pp: 2868-2878.

DOI: 10.1109/tie.2009.2036030

[6] F.J. Lin, W.J. Hwang and R.J. Wai, A supervisory fuzzy neural network control system for tracking periodic inputs,, IEEE Transaction System, Man, and Cybem, vol. 29, no. 5, pp: 583-591, (1999).

DOI: 10.1109/91.746304

[7] Rong-Jong Wai and Chia-Ming Liu, Design of Dynamic Petri Recurrent Fuzzy Neural Network and Its Application to Path-Tracking Control of Nonholonomic Mobile Robot, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 7, July 2009, pp: 2667-2683.

DOI: 10.1109/tie.2009.2020077

[8] Wang Yang, Guo Chen, Sun Fu-chun and SHEN zhi peng, Adaptibe control of ship course using a dnamic neural fuzzy algorithm, Journal of Harbin Engineering University, Vol. 30, No. 10, October, 2009, pp: 1159-1164.

[9] Yang Xu and Zeng Chenbi, Qeneralized Dynamic Fuzzy Neural Network-based Tracking Control of PV, unpublished.